Regression analysis system and regression analysis method that perform discrimination and regression simultaneously

ABSTRACT

The present invention solves a problem that there may be a case that an estimated value of regression cannot be calculated depending on a discrimination result when a regression method is applied after a discrimination method, and has a purpose to obtain an estimating equation with high accuracy even when the number of sample groups to which the regression method is applied is small. An estimating equation that satisfies the regression and discrimination at the same time can be obtained by combining a discrimination evaluation function that evaluates discrimination accuracy and a regression evaluation function that evaluates regression accuracy, calculating a combination evaluation function, and optimizing the combination evaluation function.

TECHNICAL FIELD

The present invention relates to a regression analysis system and aregression analysis method for estimating an evaluation value based on acharacteristic amount of a new sample with high accuracy by using acharacteristic amount and an evaluation value of a sample group.

BACKGROUND ART

Recently, a technology for estimating an evaluation value from acharacteristic amount calculated from data of a sample (regressionanalysis) has become more important in various industrial fields. Takinga medical field as an example, when it is possible to automaticallyestimate an evaluation scale which expresses a severity of disorder frommedical data (images of fMRI or CT, body movement information, etc.) ofa subject, it is useful as a screening test performed before a doctormakes a diagnosis. As another example, in a field of productionmanagement, it is considered a case that a level of defection isevaluated from an examination images of a product (e.g., a semiconductorcomponent) and a highly reliable component is selected. In addition, asa traffic accident prevention system, a possibility of crashing to anobject is estimated based on an image of a vehicle-mounted camera and,when the crash possibility is high, brake is applied. Hereinafter, itwill be explained taking the medical field as an example.

As described above, an algorism for estimating an evaluation value froma characteristic amount of a sample has following two steps (1) and (2)in general. In step (1), using a discrimination method, a standard todiscriminate an evaluation value estimable group (a) and an evaluationvalue inestimable group (b). Next, in step (2), targeting the samplediscriminated as the group (a) in step (1), a standard for estimating anevaluation value from a characteristic amount is obtained by using aregression method. After that, the discrimination standard of step (1)is applied to a new sample to discriminate groups and, only when it isdiscriminated as group (a), an evaluation value is estimated in step(2).

The above process is performed as follows in the medical field. In step(1), a discrimination standard is created by using characteristicamounts of a patient group (group (a)) and an unimpaired group (group(b)), and in step (2), a standard for estimating an evaluation scalefrom the characteristic amount of the patient group (group (a)) isobtained. After that, regarding a new subject in a case that theexistence of disorder or its severity are unknown, the discriminationstandard of step (1) is applied to discriminate it as unimpaired groupor patient group, and, only when it is discriminated as a patient group,the evaluation scale is estimated in step (2).

Here, as the discrimination method used in step (1), a lineardiscrimination analysis, an SVM (Support Vector Machine), and the likeare known. As the regression method used in step (2), a multipleregression analysis, an SVM regression, and the like are known.

SUMMARY OF INVENTION Technical Problem

However, in the algorism that takes two steps in this manner, there aresome problems in view of operation and accuracy. In view of operation,there is a problem that, when a new sample is mistakenly discriminatedas group (b) in step (1) even though it should be discriminated as group(a), the process does not proceed to step (2) and its evaluation valueis not calculated. As explaining in the example of the medical field,there may be a case that, when the discrimination standard of step (1)is applied to data of a new subject whose possibility of disorder isunknown, and it is discriminates as an unimpaired person but a doctordiagnose that there is a possibility of disorder. In this case, there isa problem that, even though the doctor prefers to know an estimatedevaluation value, the process does not proceed to step (2) and theevaluation value is not estimated. In addition, there may be a problemthat, when the condition transfers from a serious symptom to a mildsymptom because of a medical treatment, the evaluation value isestimated while having a serious symptom; however, when the symptombecomes milder, it may be discriminated as the unimpaired group and theevaluation value may not be estimated.

In view of accuracy, there may be a problem that the accuracy of thestandards obtained in steps (1) and (2) is lowered since the number ofthe pieces of data of sample group is small and the accuracy of a finalestimated value is further lowered due to the two steps with the lowaccuracy. In the medical field, due to the absence of data in thepatient group, especially, the accuracy of the regression in step (2) isoften lowered. It is difficult to collect data of the patient group in alarge scale since it is difficult to have an agreement of a patient, itis difficult for busy doctors to examine during diagnosing and treating,the number of patients of the same disorder vising to a hospital islimited, for example.

Solution to Problem

In order to solve the above problem, a new method for simultaneouslyrealizing the discrimination of groups (a) and (b) in step (1) and theevaluating value estimation in step (2) is necessary. In this method,the evaluation values of the groups (a) and (b) are expressed with aunified single index and the discrimination of the groups (a) and (b) isexecuted by comparing with the index and a threshold value.

Advantageous Effects of Invention

When such a method is realized, there are advantages in view ofoperation and accuracy.

In view of operation, the problem of the conventional method that theevaluation value cannot be estimated because the process does notproceed to step (2) depending on the discrimination result in step (1).In other words, there is an advantage that an evaluation value can beestimated for any samples. Explaining in the medical field, a problemthat the severity cannot be calculated due to an inconsistency betweenthe result of discrimination of the patient group and unimpaired groupin step (1) and a doctor's diagnosis dese not occur. Further, since theseverity of an unimpaired person and a patient is handled using anunified single index, the manner that the condition of the patientchanges from a serious symptom to a mild symptom because of a medicaltreatment can be followed and observed using the single index.

In view of the accuracy, there is an advantage that the accuracy ofestimating an evaluation value by using the data which was separatelyused in step (1) and step (2) at the same time. In the medical field,lowering of generalizability caused by a lack of data in the patientgroup can be reduced by using the data of the health group which iseasily corrected at the same time.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a main system configuration of afirst embodiment of the present invention.

FIG. 2 is a flowchart illustrating a main configuration of adiscrimination/regression process of the first embodiment.

FIG. 3 is a flowchart illustrating a detailed configuration of thediscrimination/regression process of the first embodiment.

FIG. 4 is a flowchart illustrating a configuration of a conventionalmethod.

FIG. 5 is a diagram illustrating a finger-tapping movement.

FIGS. 6(a) and 6(b) are waveform diagrams illustrating various waveformsof the finger-tapping movement.

FIGS. 7(a) to 7(d) are diagrams illustrating relationships betweenvarious waveform data of the finger-tapping movement and characteristicamounts calculated based from them.

FIGS. 8(a) and 8(b) are diagrams showing configurations of a samplegroup and a new sample group.

FIGS. 9(a) and 9(b) is pattern diagrams of cases that an evaluationvalue is assigned to a numerical value distribution.

FIG. 10 is a conceptual diagram explaining a regression evaluationfunction using an error sum of squares.

FIG. 11 is a conceptual diagram explaining a regression evaluationfunction using an SVM.

FIG. 12 is a conceptual diagram explaining a discrimination evaluationfunction using an error sum of squares.

FIG. 13 is a conceptual diagram explaining an discrimination evaluationfunction using a Fisher's linear discrimination analysis.

FIG. 14 is a conceptual diagram explaining a discrimination evaluationfunction using SVM.

FIGS. 15(a) to 15(c) are graphs illustrating a result of that thepresent invention is applied to a preferable finger-tapping movementdata.

FIGS. 16(a-1) to 16(c) are graphs illustrating a result of applying aconventional method to the finger-tapping movement data.

FIG. 17 is a flowchart illustrating a configuration of adiscrimination/regression process for a plurality of evaluation valuesaccording to a second embodiment of the present invention.

FIG. 18 is a conceptual diagram explaining independence of estimatedseverity of two types of disorders according to the second embodiment.

DESCRIPTION OF EMBODIMENTS

A first configuration for realizing the present invention (hereinafter,referred to as an “embodiment”) will be explained in detail withreference to the drawings according to need.

Although the present invention is applicable to data of variousindustrial fields, the present embodiment is applied to the medicalfield. The pieces of data to which the present invention is applied inthe present embodiment are finger-tapping movement data of an unimpairedgroup and a Parkinson's disease (PD) patient group and a UPDRS ft scorewhich is an evaluation of severity of the PD patient group. Thefinger-tapping movement here is a repeated movement that a patient opensand closes their thumb and index finger. The UPDRS ft is an item thatevaluates the finger-tapping movement (Finger Tapping) in a UPDRS, whichis a value grading a level of the finger-tapping movement. PD is adisease which causes a movement disorder of the entire body and symptomssuch astremor, muscle rigidity (stiffness of muscle), bradykinesia(slowness and smallness of movement) remarkably seen in movement ofpatient's fingers. Doctors visually observes finger-tapping movements ofa PD patient and evaluates the movement based on the UPDRS ft.

An outline of the present invention will be explained and a differencefrom a conventional method will be described. After that, each unit ofthe present invention will be described. Then, a result of applying thepresent invention to the above data will be shown.

Principle Units of First Embodiment

A system configuration of a first embodiment of the present invention isillustrated in FIG. 1. Measurement target data is measured by ameasurement device 60 and imported to a processing device 70. In acharacteristic amount extraction device 73, a later describedcharacteristic amount is extracted from imported original data. Thecharacteristic amounts extracted from respective pieces of sample dataand evaluation values applied to the sample data are stored in a memory73. The processing device 70 executes processes to optimize anestimating equation for calculating an estimated evaluation value from anew sample using accumulated values of the special value and evaluationvalue, and at the same time, to calculate an estimated evaluation valuefrom a characteristic amount of the new sample based on the estimatingequation. Here, this process is referred to as adiscrimination/regression process.

FIG. 2 illustrates a flow of the discrimination/regression process. Therespective blocks for executing the discrimination/regression processillustrated in FIG. 1 are also represented by the same reference numbersin FIG. 2. Here, the present embodiment will be explained with referenceto FIG. 2. Characteristic amounts 201 and evaluation values 202 of (allor a part of) a sample group 2 stored in the memory are introduced to aregression evaluation function calculation unit 14 and the regressionevaluation function calculation unit 14 calculates a regressionevaluation function 16. In the same manner, the characteristic amounts201 and evaluation values 202 of (all or a part of) the sample group 2are introduced to a discrimination evaluation function calculation unit15 and a discrimination evaluation function 17 is calculated. Then, theregression evaluation function 16 and the discrimination evaluationfunction 17 are input to the combination evaluation function calculationunit 18 and a combination evaluation function 20 is calculated. Then, acombination evaluation function optimization unit 21 optimizes thecombination evaluation function 20 so that an estimating equation 24 iscalculated. Then, an estimating equation application unit 25 applies acharacteristic amount 301 of a new sample 3 to the estimating equationto calculate an estimated evaluation value 5.

As described above, when discrimination and regression are executed atthe same time, two problems of the conventional method are solved. Thefirst problem is that, since a regression process is performed after adiscrimination process in the conventional method, an estimation valueof the regression is not calculated depending on the discriminationresult. According to the first embodiment, since discrimination andregression are performed simultaneously, evaluation values can beestimated for all samples. The second problem is that, in theconventional regression process, the estimation accuracy of theregression process is reduced when the number of samples having a usableevaluation value is small. According to the present invention, sincesamples lacking a evaluation value can be used in discrimination, thenumber of usable samples increases and the estimation accuracy isimproved.

<<Additional Units to Improve Accuracy>>

Further, according to the present embodiment, as illustrated in FIG. 3,by adding following four units to the flow of FIG. 2, the accuracy ofthe estimating equation can be improved. The four added units are anevaluation value conversion unit 10, a discrimination/regressionpriority adjusting unit 19, a convergence determination unit 22, and animportant characteristic amount selection unit 23. The four units may beseparately added to the flow of FIG. 2 or added at once. Hereinafter,configurations and effects of the four units will be explained.

The first evaluation value conversion unit 10 is a unit for convertingthe evaluation value 202 of the sample group 2 into a numerical value, anumerical value distribution, or a numerical value range before theregression evaluation function calculation unit 14 and discriminationevaluation function calculation unit 15. The evaluation value conversionunit 10 includes an evaluation value substitution table creation unit11, an evaluation value substitution unit 12, and a sample assignmentunit 13. The evaluation value substitution table creation unit 11creates a table that associates the evaluation value 202 of the samplegroup 2 with a numerical value, a numerical value distribution, or anumerical value range. This table includes a case of the sample group 2which lacks the evaluation value 202. The evaluation value substitutionunit 12 substitutes the evaluation value 202 of the sample group 2 witha numerical value, a numerical value distribution, or a numerical valuerange based on the above table. The sample assignment unit 13 assignsthe sample of the sample group 2 as a sample input to the regressionevaluation function calculation unit 14 and a sample input to thediscrimination evaluation function calculation unit 15. Here, there maybe a sample to be input to both of the regression evaluation functioncalculation unit 14 and discrimination evaluation function calculationunit 15.

Effect of the evaluation value conversion unit 10 will be explained.When the evaluation value 202 of the sample group 2 given as a numericalvalue in advance is converted to a numerical value distribution or anumerical value range, discreteness of the evaluation values can bereduced. Thus, regression and discrimination with an evaluation valuethat is close to an actual condition can be executed and the accuracy ofestimating equation is improved. Further, in a case when the evaluationvalue is absent, the accuracy of the estimating equation is improved byapplying a tentative numerical value, a numerical value range, or anumerical value distribution.

Next, the second discrimination/regression priority adjusting unit 19will be explained. The discrimination/regression priority adjusting unit19 is a unit for adjusting priority between the discrimination andregression when the combination evaluation function calculation unit 18combines the regression evaluation function 16 and the discriminationevaluation function 17. The priority is adjusted based on a magnitude ofa priority constant 1901. Here, the priority constant 1901 is anumerical value searched by a priority constant search unit 1902 so asto maximize the accuracy of the estimating equation. Note that thepriority constant 1901 may be a predetermined constant.

Effect of the discrimination/regression priority adjusting unit 19 willbe explained. Firstly, there is an advantage that the priority can bespecified in a case that one of the discrimination and regression needsto be prioritized. Further, the estimation accuracy can be furthermaximized by using an estimation accuracy of the estimating equation 24obtained from the combination evaluation function optimization unit 21in a calculation process in the priority constant search unit 1902.

Next, the third convergence determination unit 22 will be explained.This unit determines whether the optimized result by the combinationevaluation function optimization unit 21 sufficiently converges, andwhen the convergence is not sufficient, feedback is given to theevaluation value substitution table creation unit 11 in the evaluationvalue conversion unit 10. Based on the feedback, the numerical value,numerical value distribution, or numerical value range used tosubstitute the evaluation value is corrected. The feedback iscontinuously given until it is determined that the optimization by thecombination evaluation function 20 sufficiently converges.

The effect of the convergence determination unit 22 is that the accuracyof a conclusively-output estimating equation 24 can be improved bycorrecting the table for substituting the predetermined evaluation valuewith a numerical value, a numerical value distribution, or a numericalvalue range based on the result of the combination evaluation functionoptimization unit 21.

Last of all, the fourth important characteristic amount selection unit23 will be explained. The important characteristic amount selection unit23 is a unit for selecting, from the combination evaluation functionoptimization unit 21, an important characteristic amount that has aninfluence on the estimation accuracy.

Effect of the important characteristic amount selection unit 23 will bedescribed. It is assumed that, by notifying the important characteristicamount 4 output from the important characteristic amount selection unit23 to the characteristic amount 201 of the sample group 2 as feedback,data of only the important characteristic amount 4 is selected and thediscrimination/regression process is executed again. With this method,multicollinearity which may be caused when there are many characteristicamounts in regression or discrimination can be avoided and theestimation accuracy can be improved. Here, only the importantcharacteristic amount 4 may be output without giving feedback to thecharacteristic amount 201.

<<Comparison with Conventional Method>>

Here, referring to a flow of a conventional method illustrated in FIG.4, a difference from the flow of the present invention will beexplained. In the conventional method, after discrimination is executedbetween an unimpaired group or a patient group in a discriminationprocess 101 (discrimination analysis and the like), a regression process102 (multiple regression analysis and the like) is applied and severityis calculated only when it is discriminated as the patient group.

Firstly, the discrimination process 101 is applied to a characteristicamount 10301 of a sample group (1). Inside the discrimination process101, a discrimination evaluation function calculation unit 1011calculates a discrimination evaluation function 1012. Then, adiscrimination evaluation function 1012 is optimized by a discriminationevaluation function optimization unit 1013 so that a discriminationequation 1014 is calculated.

Next, independently from the discrimination process 101, a regressionprocess is applied to a characteristic amount 10501 and an evaluationvalue 10502 of a sample group (2). In the regression process 102, theregression evaluation function calculation unit 1021 calculates aregression evaluation function 1022. Then, a regression evaluationfunction 1022 is optimized by the regression evaluation functionoptimization unit 1023 so that an estimating equation 1024 iscalculated.

Regarding data of a new subject (new sample 104) of a case that theexistence of disorder or its severity is unknown, a discriminationequation 1014 is firstly applied by a discrimination equationapplication unit 1015 and it is discriminated to be in an unimpairedgroup or a patient group. Next, only when it is discriminated to be apatient group, an estimating equation application unit 1025 applies anestimating equation 1024, and an estimated evaluation value 106 iscalculated. In the conventional method, in this manner, the estimatedevaluation value 106 is calculated only when the discrimination equationapplication unit 1015 discriminates as a patient group. In contrast,according to the present invention, an estimated evaluation value 5(FIG. 2 or FIG. 3) is calculated for every sample.

<<Characteristic Amount and Evaluation Value>>

[Characteristic Amount]

Inputs of the discrimination/regression process according to the presentinvention are a characteristic amount and an evaluation value. Firstly,a characteristic amount is described.

The characteristic amount is one or more numerical value that iscalculated from original data obtained from a sample. Here, originaldata includes any data such as an image, sound, an electrical voltage, aquestionnaire result, and the like as long as data can be expressed by anumerical value. Even category data may be included in original data ifit can be expressed by a numerical value. For example, in the medicalfield, there are a medical image taken by an MRI, a CT or amagnetocardiographic, a waveform measured by an electrocardiograph, acomponent value of a blood test, a questionnaire for a patient, and thelike.

According to the present embodiment, a characteristic amount calculatedfrom finger-tapping movement data is used. The finger-tapping movementis a movement to repeatedly open and close a thumb and an index fingeras illustrated in FIG. 5. A state 41 that two fingers are opened and astate 42 that the two fingers are closed are repeated alternately.According to the present embodiment, magnetic sensors 43 are attached tothe thumb and index finger respectively and a distance 44 between thetwo fingers is measured. The magnetic sensor is a sensor including twocoils and one of the coil receives a magnetic field generated by theother coil so that a distance between two coils is measured.

FIG. 6(a) illustrates waveforms of typical finger tapping of anunimpaired person; and FIG. 6(b) illustrates waveforms of typical fingertapping of a PD patient. A distance waveform 51 is converted from outputvoltage of the magnetic sensor. By differentiating the distance waveform51, a velocity waveform 52 and an acceleration waveform 53 are obtained.Based on these waveforms, it is understood that the unimpaired personsmoothly repeats the opening and closing movements. On the other hand,it is understood that the PD patient has movements different from thoseof the unimpaired person due to symptoms such as a stiffness of muscle(muscle rigidity), a rhythm disorder, and the like. Since there areremarkable differences between the finger-tapping movements of theunimpaired person and the PD patient, it is used for doctor's diagnosisby visual observation (UPDRS ft) as described above.

Based on these waveforms, 21 characteristic amounts illustrated in FIG.7 (7(a)-7(d)) are calculated. From the distance waveforms of FIG. 7(a),following five characteristic amounts are calculated. A maximumamplitude of distance (1) is a difference between a maximum value and aminimum value of the distance waveform. A total travel distance (2) is asum of absolute values of distance change amounts within a totalmeasurement time. Then, an average of local maximum values of distance(3) is an average value of a local maximum values of finger-tappingmovements (illustrated in FIG. 7), and a standard deviation of localmaximum values of distance (4) is a standard deviation of local maximumvalues of every finger-tapping movement. An approximate straight lineinclination of distance local maximum point (5) is an inclination of anapproximate straight line of a local maximum point (illustrated in FIG.7) and is supposed to mainly express a change of the amplitude caused bytiredness during the measurement time.

Similarly, from velocity waveforms (FIG. 7(b)) obtained bydifferentiating the distance waveforms, following seven characteristicamounts are calculated. A velocity maximum amplitude (6) is a differencebetween a maximum value and a minimum value of velocity waveforms. Anaverage of local maximum values of velocity (7) is an average value oflocal maximum values of every finger-tapping movement, and an average oflocal minimum values of velocity (8) is an average value of localminimum values of every finger-tapping movement. Similarly, a standarddeviation of local maximum values of velocity (9) is a standarddeviation of local maximum values of every finger-tapping movement, anda standard deviation of local minimum values of velocity (10) iscalculated as a standard deviation of local minimum values of everyfinger-tapping movement. Here, the local maximum value of velocity is amaximum value in an opening operation (from a condition that two fingersare closed to a condition that the two fingers are fully opened), andthe local minimum value of velocity is a minimum value in a closingoperation (from a condition that two fingers are opened and to acondition that the two fingers are closed). Further, an energy balance(11) is a ratio of a square of velocity during the opening operation anda square of velocity during the closing operation. A total energy value(12) is a square of velocity during the entire measurement time.

Further, regarding the acceleration waveforms of FIG. 7(c) obtained bydifferentiating the velocity waveform, following five characteristicamounts are calculated. The maximum amplitude of acceleration (13) is adifference between a maximum value and a minimum value of theacceleration waveforms. Further, focusing on four types of extremevalues found in one cycle of tapping, an average of local maximum valuesof velocity in an opening operation (14), an average of local maximumvalues of velocity in an opening operation (15), an average of localmaximum values velocity in a closing operation (16), and an average oflocal minimum value of velocity in a closing operation (17) arecalculated (all values are illustrated in FIG. 7(c)). Thesecharacteristic amounts respectively correspond to operating forces at atiming that the two fingers start to open, a timing that the fingers areopened, a timing that the fingers are closed, and a timing that thefingers start to close.

In final, from data of tapping intervals of FIG. 7(d), fourcharacteristic amounts are calculated. A number of tapping (18) is anumber of finger-tapping movements during the entire measurement time. Atapping interval average value (19) is an average value of tappingintervals (illustrated in FIG. 7(d)) which are intervals from a localminimum point to a next local minimum point of the distance waveforms.An advantage frequency (20) is a frequency that a spectrum becomesmaximum when the distance waveform is converted by Fourier transform. Astandard deviation of tapping intervals (21) represents a standarddeviation of the tapping intervals.

[Evaluation Value]

Next, the evaluation value will be described. The evaluation value is anumerical value which is previously given to a sample. It may be anumerical value obtained from scoring by a person or may be a numericalvalue obtained from an experimental result. It may be any index if it isa numerical value obtained by evaluating a sample based on apredetermined standard.

According to the present embodiment, as an evaluation value, the UPDRSft which is an evaluation scale scored by a doctor is used. The UPDRS ftis an item of UPDRS which is a PD evaluation scale and an integer valueof five levels of 0≤UPDRS ft≤4. In case that UPDRS ft=0, thefinger-tapping movement is understood to be normal and, as it becomescloser that UPDRS ft=4, increases severity is indicated.

In the medical field, in addition to the UPDRS used in the presentembodiment, there are various evaluation scales such as Yahr's severityclassification used for PD diagnosis, a UHDRS (Unified Huntington'sDisease Rating Scale) used for Huntington's disease diagnosis, a SARA(Scale for the Assessment and Rating of Ataxia) used for an ataxiadiagnosis, a MMSE (Mini-Mental State Examination) used for a dementiadiagnosis, and the like.

Sample Group Used in the Present Embodiment

As a sample group to which the present invention is applied,finger-tapping movements were tested with an unimpaired group of 196individuals (males and females of age 50 to 70) and a PD patient groupof 28 individuals (males and females of age 60 to 70). As describedabove, since it is difficult to obtain data of a patient group, thenumber of individuals in the PD patient group is a small number of 28.In the test, an instruction “as wider as possible and as fast aspossible” was given and finger-tapping movements of a right hand wastested for 30 seconds. Based on obtained waveforms, the above 21characteristic amounts were calculated. Further, a UPDRS ft score that adoctor visually observed and scored the finger-tapping movement wasrecorded as an evaluation value.

Characteristic amounts and evaluation values obtained in the abovesample group are described in the diagram of FIGS. 8(a) and 8(b). Asillustrated in FIG. 8(a), in the unimpaired group, each unimpairedindividual has number of characteristic amounts and an evaluation valueis not given. In the PD patient group, each PD patient is given with “p”number of characteristic amounts and an evaluation value. The samplegroups in FIG. 8(a) are input to the discrimination/regression process 1as the sample group 2 of FIG. 2, and characteristic amounts of a newsample of FIG. 8(b) are input to the estimating equation applicationunit 25 of FIG. 2 as the new sample 3 of FIG. 2 so that the estimatedevaluation value 5 is calculated.

<<Definition of Estimating Equation>>

Hereinafter, a configuration and a concrete application method of thepresent invention will be explained.

According to the present embodiment, the estimating equation is anequation in which a characteristic amount is linearly-combined asEquation 1.

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 1} \right\rbrack\mspace{211mu}} & \; \\{\mspace{79mu}{{y_{e}(x)} = {w_{0} + {\sum\limits_{p = 1}^{P}{w_{p}x_{p}}}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 1} \right)\end{matrix}$

In this equation, x_(p) is characteristic amounts (n=0 to P, P=thenumber of characteristic amounts) obtained from finger-tapping movementdata, w_(p) is weight corresponding to each characteristic amount, andw₀ is a constant term. The characteristic amount x_(p) is acharacteristic amount after normalizing to cancel a difference in arange among the characteristic amounts. When the characteristic amountbefore normalization is expressed as x_(rp), x_(p) can be calculated byx_(p)=(x_(rp)−m_(p))/σ_(p) using an average value m_(p) and a standarddeviation σ_(p) of x_(rp) of the unimpaired group. Here, the value ofthe original characteristic amount may be used without the normalizationin this manner.

The method for creating a new index by linearly combining a plurality ofcharacteristic amounts in this manner is used in many conventionalprocesses such as a discrimination analysis, a multiple regressionanalysis, and the like. In the present invention, in addition to theestimating equation by linear combination, other formats may be employedif it is an equation in which a plurality of characteristic amounts areinput and a single numerical value is calculated. For example, anestimating equation using a basis function φ(x_(p)) of one of (Equation2a), (Equation 2b) or (Equation 2c) may be applied as substitute forx_(p) in the right side of (Equation 1).

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2a} \right\rbrack\mspace{185mu}} & \; \\{\mspace{79mu}{{\phi\left( x_{p} \right)} = {\sum\limits_{q = 1}^{Q}{d_{p}^{q}x_{p}^{q}}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2a} \right) \\{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2b} \right\rbrack\mspace{185mu}} & \; \\{\mspace{79mu}{{\phi\left( x_{p} \right)} = {\exp\left\{ {- \frac{\left( {x_{p} - \mu_{p}} \right)^{2}}{2\sigma_{p}^{2}}} \right\}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2b} \right) \\{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2c} \right\rbrack\mspace{185mu}} & \; \\{\mspace{79mu}{{\phi\left( x_{p} \right)} = \frac{1}{1 + {\exp\left\{ {- \frac{x_{p} - \mu_{p}}{\sigma_{p}}} \right\}}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2c} \right)\end{matrix}$

(Equation 2a) expresses the basis function φ(x_(p)) of linearcombination as a polynomial equation. Further, (Equation 2b) expressesthe basis function as a Gaussian distribution, and (Equation 2c)expresses the basis function as a logistic sigmoid function. Here, sincea usage of a kernel method in an optimization of a later describedcombination evaluation function sometimes makes the calculation easier,the estimating equation may be defined using the kernel method.

Here, in the present embodiment, a single estimating equation is enoughsince the evaluation value is one type; however, more than oneestimating equation need to be defined when a plurality of evaluationvalues are used.

<<Conversion of Evaluation Value>>

The evaluation value conversion unit 10 (FIG. 3) will be explained. Theevaluation value conversion unit 10 includes the evaluation valuesubstitution table creation unit 11, the evaluation value substitutionunit 12, and the sample assignment unit 13. In the present embodiment,since the UPDRS ft is not evaluated for the unimpaired group, theevaluation value is absent. Thus, based on following interpretation, theevaluation value is converted into a numerical value range.

The UPDRS ft is expressed as integer values from 0 to 4 and 0 is definedas unimpaired and the number closer to 4 is defined as severer.Considering based on these definitions, when a finger-tapping movementof an unimpaired person is evaluated by the UPDRS ft, it is presumed tobe equal to or lower than 0. In other words, as setting UPDRS ft=0 as aborder between a PD patient and an unimpaired group, the range of UPDRSft≤0 is considered to be in the unimpaired group, and the range of UPDRSft>0 is considered to be in the PD patient group. As described above,according to the present embodiment, evaluation values which are absentin the unimpaired group are converted into a numerical value range whichis UPDRS ft≤0.

Here, according to the present embodiment, the loss of the evaluationvalue is made associated with a numerical value range restricted by asingle inequality equation; however, it may be made associated withother numerical values or numerical value distributions. For example, itmay be made associated with a numerical value range, which is restrictedby two inequality equations using upper and lower restrictions, afunction such as a normal distribution, or the like.

Further, in the present embodiment, the absence of the evaluation valueis converted; however, an evaluation value without absence may beconverted into a numerical value distribution. For example, regardingthe PD patient group to which a UPDRS ft score is given in advance, itmay be considered a case to convert (evaluation value UPDRS ft=0) into(−0.5≤UPDRS ft<0.5), (evaluation value UPDRS ft=1) into (0.5≤UPDRSft<1.5), (evaluation value UPDRS ft=2) into (1.5≤UPDRS ft<2.5),(evaluation value UPDRS ft=3) into (2.5≤UPDRS ft<3.5), and (evaluationvalue UPDRS ft=4) into (3.5≤UPDRS ft<4.5). FIGS. 9(a) and 9(b) areconceptual diagrams illustrating a conversion of the evaluation valuesinto numerical value distributions. In other words, FIG. 9(a)illustrates a correspondence relationship between unconverted evaluationvalues and estimated evaluation values, and FIG. 9(b) illustrates acorrespondence relationship between the evaluation values which arealready converted into numerical value distributions and the estimatedevaluation values.

When the evaluation value given as a numerical value is substituted witha numerical value distribution in this manner, there is an advantagethat discreteness of the evaluation values can be reduced. The reductionof the discreteness will be concretely described. Even when somesubjects have the same evaluation value, some of them may be subjectswith a mild symptom and some may be subjects with a sever symptom.However, since the doctor evaluates by a visual observation, it isdifficult to grade in a more detailed evaluation scale than the currentfive levels. Here, this problem is solved by substituting the evaluationvalue with a numerical value distribution. Concretely, as illustrated inFIG. 8(b), when (UPDRS ft=1) is substituted with (0.5≤UPDRS ft<1.5),among the subjects evaluated as (UPDRS ft=1), a subject with a mildersymptom may be given an evaluation value closer to (UPDRS ft=0.5) and asubject with a severer symptom may be given an evaluation value closerto (UPDRS ft=1.5), so that the evaluation scale can fits the reality.When the discreteness is reduced in this manner, regression effect (aphenomenon that an estimated value becomes closer to an average valuewhen an error within a sample group is large) can be reduced.

Considering the present embodiment in the same manner, while theunimpaired group is evaluated all (UPDRS ft=0) if a doctor evaluates, itmay be considered that the discreteness was reduced by substituting witha numerical value distribution of (UPDRS ft≤0).

Here, according to the present embodiment, a numerical value, anumerical value distribution, or a numerical value range is associatedwith a single sample; however, two or more of the numerical value,numerical value distribution and numerical value range may be associatedwith a single sample. By doubly associating in this manner, when thesame sample is used in both of the regression and discrimination, theevaluation value can be calculated as a numerical value range in thediscrimination evaluation function and the evaluation value can becalculated as a numerical value in the regression evaluation function.

<<Calculation of Combination Evaluation Function>>

A method for calculating a combination evaluation function 20(E) will beexplained. For the explanation, a discrimination evaluation function17(E_(d)) for evaluating the discrimination accuracy between theunimpaired group and the patient group and a regression evaluationfunction 16(E_(r)) for evaluating the accuracy of a severityquantification of the patient group, which are required in the processof E calculation, will be defined. Hereinafter, both calculation methodswill be explained in order of the regression evaluation function E_(r)and the discrimination evaluation function E_(d).

[Calculation of Regression Evaluation Function]

The regression evaluation function calculation unit 14 (FIG. 1, FIG. 2,or FIG. 3) will be explained. The regression evaluation function16(E_(r)) is an evaluation function that expresses a severityquantification of the patient group. Here, E_(r) is made to be the sameas an error function defined in the multiple regression analysis. Inother words, as expressed in (Equation 3a), a summation of square of anerror between the evaluation scale y_(ri) and the estimated evaluationvalue y_(ei) is calculated for all samples of the patient group (i=1 toN_(r), N_(r) is the number of samples used in regression).

[Mathematical  Formula  3a]                                                        (Mathematical  Formula  3a)$\mspace{79mu}{E_{r} = {\sum\limits_{i = 1}^{N_{r}}{\left( {y_{ri} - y_{ei}} \right)^{2}\mspace{14mu}{where}}}}$$\mspace{79mu}\left\{ \begin{matrix}{N_{r}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{regression}} \\{y_{ri}\text{:}\mspace{14mu}{previously}\mspace{14mu}{given}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}\left( {i = {1\mspace{14mu}{to}\mspace{14mu} N_{r}}} \right)} \\{y_{ei}\text{:}\mspace{14mu}{estimated}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}\left( {i = {1\mspace{14mu}{to}\mspace{14mu} N_{r}}} \right)}\end{matrix} \right.$

FIG. 10 illustrates a concept of calculation of regression evaluationfunction using the error sum of squares. As understood from thedefinition of (Equation 3a), E_(r) represents a degree of diremption ofthe estimated evaluation value y_(e) from the evaluation scale y_(r). Inother words, as E_(r) becomes smaller, the accuracy of the estimatedevaluation value y_(e) increase. Thus, in order to improve the accuracyof severity quantification of the patient group, E_(r) needs to beminimized.

Other equation as a substitute for the equation shown as (Equation 3a)may be used, as long as E_(r) is an evaluation function that expressesthe accuracy of the severity quantification of the patient group. Forexample, in order to avoid over-fitting, a case may be considered thatan evaluation function like (Equation 3b) is used by adding aregularization term (square sum of factor w_(n) of the estimatingequation, or the like).

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 3b} \right\rbrack\mspace{185mu}} & \; \\{\mspace{79mu}{{E_{r} = {{\sum\limits_{i = 1}^{N_{r}}\left( {y_{r} - y_{e}} \right)^{2}} + {\lambda{w}^{2}\mspace{14mu}{where}}}}\mspace{79mu}{w = \left( {w_{0},w_{1},{\ldots\mspace{14mu} w_{p}}} \right)}{\lambda \geq {0\text{:}\mspace{14mu}{constant}\mspace{14mu}{of}\mspace{14mu}{regularization}\mspace{14mu}{term}}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 3b} \right)\end{matrix}$

As another example of a definition of E_(r), an evaluation function suchas an equation of (Equation 3c) may be considered.

[Mathematical  Formula  3c]                                                          (Mathematical  Formula  3c)$\mspace{79mu}{E_{r} = {{\sum\limits_{i = 1}^{N_{r}}{\xi\left( {y_{ri} - y_{ei}} \right)}} + {\frac{\lambda}{2}{w}^{2}\mspace{14mu}{where}}}}$$\mspace{79mu}\left\{ \begin{matrix}{N_{r}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{regression}} \\{y_{ri}\text{:}\mspace{14mu}{previously}\mspace{14mu}{given}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}\left( {i = {1\mspace{14mu}{to}\mspace{14mu} N_{r}}} \right)} \\{y_{ei}\text{:}\mspace{14mu}{estimated}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}\left( {i = {1\mspace{14mu}{to}\mspace{14mu} N_{r}}} \right)} \\{w = \left( {w_{0},w_{1},{\ldots\mspace{14mu}{\overset{.}{w}}_{P}}} \right)} \\{{\xi(z)} = \left\{ \begin{matrix}0 & {{{if}{z}} < ɛ} \\{{z} - ɛ} & {otherwise}\end{matrix} \right.}\end{matrix} \right.$

This evaluation function is an evaluation function related to a marginmaximization used in an SVM regression (Support Vector MachineRegression). In other words, as illustrated in FIG. 11, two hyperplanesbeing separated from estimating equation equal to or more than E arepresumed and a penalty in proportion to a distance from the hyperplanesis given only to a sample distributed outside the hyperplanes. Here,regarding E_(r), when the number of samples having the same evaluationvalue differs, it is preferable to regularize each group with the numberof samples. This prevents that the range of E_(r) changes according tothe number of input samples. There is an advantage that a laterdescribed priority constant is not easily affected by the number ofsamples when the regularization is executed. Further, when there are aplurality of evaluation values to be processed in regression and aplurality of estimating equations are defined accordingly, E_(rk) (krepresents each evaluation value) is respectively defined and eachE_(rk) is weighted and added to calculate E_(r). Here, as the method forcombining E_(rk), other methods may be employed.

[Calculation of Discrimination Evaluation Function]

Next, the discrimination evaluation function calculation unit 15 (FIG.1, FIG. 2 or FIG. 3) will be explained. When only E_(r) is minimized asdescribed above, the accuracy of the discrimination for the patientgroup and the unimpaired group may not be increased at the same timeeven when the accuracy of the severity quantification improves. Thus,the discrimination evaluation function 17(E_(d)) for evaluating theaccuracy of the discrimination for the unimpaired group and patientgroup, which is expressed as (Equation 4), is introduced.

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 4} \right\rbrack\mspace{211mu}} & \; \\{\mspace{79mu}{{E_{d} = {\sum\limits_{i,{y_{ei} > 0}}^{N_{d}}{\left( {y_{ei} - 0} \right)^{2}\mspace{14mu}{where}}}}{N_{r}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{regression}}{y_{ei}\text{:}\mspace{14mu}{estimated}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}\left( {i = {1\mspace{14mu}{to}\mspace{14mu} N_{d}}} \right)}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 4} \right)\end{matrix}$

This equation expresses that a summation of square of an error from 0 iscalculated for only data of (estimated evaluation value y_(ei)>0) in theunimpaired group.

When the regression evaluation function E_(r) is minimized, in thepatient group, since the estimated evaluation value y_(e) becomes closeto the evaluation scale y_(r) of the patient group, it basically becomes(y_(e)>0). Based on the above, when (y_(e)≤0) is satisfied in theunimpaired group in contrast, it enables to discriminate the patientgroup and the unimpaired group by using y_(e). According to thisconsideration, (Equation 4) selects only data in which (y_(e)≤0) is notsatisfied (that is, data of (y_(e)>0)) in the unimpaired group and givesa greater penalty to those being further from (y_(e)=0) (FIG. 11). Thus,when the discrimination evaluation function E_(d) is minimized, sincemany pieces of data in the unimpaired group satisfy (y_(e)≤0), itbecomes easier to discriminate the unimpaired group from the patientgroup which is (y_(e)>0). In this manner, the discrimination evaluationfunction calculation unit 15 calculates E_(d) so as to satisfied thelimitation, targeting a sample in which its evaluation value issubstituted with a numerical value range or a numerical valuedistribution by the evaluation value conversion unit 10.

In addition to the above, other evaluation functions may be defined ifit is an evaluation function expressing the accuracy of discriminationbetween two or more groups. For example, as expressed by (Equation 5),an evaluation function which is used in Fisher's linear discriminationanalysis may be employed.

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 5} \right\rbrack\mspace{211mu}} & \; \\{\mspace{79mu}{{E_{d} = {\frac{S_{B}}{S_{W}} = {\frac{\left( {m_{2} - m_{1}} \right)^{2}}{s_{1}^{2} + s_{2}^{2}}\mspace{14mu}{where}}}}\mspace{79mu}\left\{ \begin{matrix}{m_{k} = {\frac{1}{n_{k}}{\sum\limits_{y_{e} \in C_{k}}y_{e}}}} \\{s_{k} = \sqrt{\sum\limits_{y_{e} \in C_{k}}\left( {y_{e} - m_{k}} \right)^{2}}} \\{C_{k}\text{:}\mspace{14mu}{class}\mspace{14mu}\left( {{k = 1},2} \right)}\end{matrix} \right.}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 5} \right)\end{matrix}$

This evaluation function means a ratio of a between-class variance S_(B)in a within-class variance S_(w). Here, the between-class variance S_(B)expresses an average value of a plurality of groups and the within-classvariance S_(w) expresses a variability within each group (see FIG. 12).With a larger between-class variance S_(B) and a smaller within-classvariance S_(w), the two classes can be discriminated with a higheraccuracy. Thus, when the evaluation function is maximized, an estimatingequation with a high discrimination performance can be obtained.

In addition to the above, an evaluation function like (Equation 6) maybe employed.

[Mathematical  Formula  6]                                                          (Mathematical  Formula  6)$\mspace{79mu}{E_{d} = {{\sum\limits_{i = 1}^{N_{d}}\xi_{i}} + {\frac{\lambda}{2}{w}^{2}\mspace{14mu}{where}}}}$$\mspace{79mu}\left\{ \begin{matrix}{w = \left( {w_{0},w_{1},{\ldots\mspace{14mu} w_{P}}} \right)} \\{N_{d}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{discrimination}} \\{\xi_{i} \geq 0} \\{{t_{i}y_{ei}} \geq {1 - \xi_{i}}} \\{t_{i} = \left\{ \begin{matrix}1 & \left( {{class}\mspace{14mu} 1} \right) \\{- 1} & \left( {{class}\mspace{14mu} 2} \right)\end{matrix} \right.}\end{matrix} \right.$

This evaluation function is an evaluation function related to a marginmaximization used in a discrimination by the SVM. When the evaluationfunction is maximized, as illustrated in FIG. 14, with a hyperplane(y_(e) (x)=0) as a border, two classes of class 1 and class 2 can bediscriminated (class 1: t_(i)=1, class 2: t_(i)=−1). In (Equation 6), inorder to realize a flexible discrimination as accepting an errordiscrimination, two hyperplanes (y_(e)(x)=1) and (y_(e)(x)=−1) beingseparated from the border at a certain distance are presumed, a sampledistributed outside the hyperplanes is considered as an errordiscrimination, and a penalty in proportion to a distance ξ_(i) from thehyperplanes is given to the sample. Here, it is preferable that E_(d) isregularized with the number of samples. This is to prevent that therange of E_(d) changes according to the number of input samples. Thereis an advantage that, when a regularization is executed, a laterdescribed priority constant c is not easily affected by the number ofsamples. Further, when discriminating three or more groups, the abovecalculation is executed for two groups respectively to calculate E_(dk)(k is a combination of two groups) and then each E_(dk) is combined tocalculate E_(d).

[Combining Regression Evaluation Function and Discrimination EvaluationFunction]

The combination evaluation function calculation unit 18 (FIG. 1, FIG. 2or FIG. 3) will be explained. As described above, the regressionevaluation function 16 (E_(r)) that expresses the accuracy of thediscrimination between the unimpaired group and the patient group, andthe discrimination evaluation function 17 (E_(d)) that expresses theaccuracy of the severity quantification of the patient group aredefined, and it has been described that the functions needs to beminimized. However, in general, since w_(n) that optimizes E_(d) andw_(n) that optimizes E_(r) cannot be equal, E_(d) and E_(r) cannot beoptimized at the same time. Thus, c₁ and c₂ are introduced as priorityconstants 1901 to adjust the priority of those functions and acombination evaluation function E like (Equation 7a) is defined so thatE is optimized.[Mathematical Formula 7a]E=c ₁ E _(d) +c ₂ E _(r) where c ₁ , c ₂: priorityconstant  (Mathematical Formula 7a)

Here, c₂ is made to be a large value to emphasize the accuracy of theseverity quantification and, on the other hand, c₁ is made to be a largevalue to emphasize the accuracy of the discrimination between thepatient group and the unimpaired group. Further, ultimately, it may beset as (c₂=0) to eliminate the effect of the severity quantification andit may be set as (c₁=0) to eliminate the effect of the discrimination.These cases are the same as the case in which the discrimination processor the regression process is applied respectively.

Here, in the present embodiment, two constants of c₁ and c₂ are set asthe priority constants 1901, the number of the priority constants 1901is not limited to two. For example, as an equation illustrated as(Equation 7b), it may be considered a case that E is defined bycalculating a product of E_(d) and E_(r) using c.[Mathematical Formula 7b]E=E _(d) ^(c) E _(r) ^((1-c)) where c ₁ , c _(2:) priorityconstant  (Mathematical Formula 7b)

Further, E may be calculated by using only E_(d) and E_(r) withoutsetting the priority constant 1901. In this case, a later describedprocess for adjusting the priority of the regression and discriminationis not necessary.

[Priority Adjustment of Regression and Discrimination]

The discrimination/regression priority adjusting unit 19 (FIG. 2) willbe explained. In the present embodiment, the priority constants 1901 areset as (c₁=0.2) and (c₂=1.0). However, c₁ and c₂ may be a searchednumerical value which maximizes discrimination performance or regressionperformance without using a predetermined numerical value. For example,regarding the discrimination accuracy, based on an AUC (Area Under theROC Curve) as a standard, it may be considered a method for searching cso that the AUC becomes maximized using a golden section method. Inaddition, a summation of squared error of an estimated evaluation valueestimated using the estimating equation obtained by optimizing E and anevaluation value given to the sample group may be minimized. Theseevaluations are preferably executed by using a LOO method (Leave one outmethod), a cross validation method and the like.

Further, in addition to the golden sectional method, the method foroptimizing the index expressing the accuracy of the estimating equation24 may be any method such as a Newton's method, a quasi-Newton's method,a simplex method, a neural network, or the like as long as the methodcan optimize the function.

<<Optimization of Combination Evaluation Function>>

The combination evaluation function optimization unit 21 (FIG. 1, FIG. 2or FIG. 3) will be explained. The above described combination evaluationfunction 20(E) is minimized by the quasi-Newton's method. Thequasi-Newton's method is a method to reduce a calculation amount byapproximating an inverse matrix of a Hessian matrix used in the Newton'smethod by a BFGS formula. As a step size used in the quasi-Newton'smethod, the golden section method which is a type of a primary searchmethod is used. The method for sequentially minimizing a function, suchas the quasi-Newton's method, is used in this manner as a method forminimizing the combination evaluation function E in this manner becausethe combination evaluation function cannot be analytically optimized ingeneral. When the combination evaluation function E is defined to beanalytically optimized, a sequential solution method is not necessary tobe used.

Here, according to the present embodiment, the optimistic estimatingequation 24 is obtained by minimizing the combination evaluationfunction E; however, when an evaluation function, which realizesregression or discrimination by maximizing in a manner of the equationof (Equation 3c) or the equation of (Equation 6), is used, thecombination evaluation function E needs to be maximized. Further,according to the present embodiment, the quasi-Newton's method is usedto optimize E; however, other optimization methods may be employed. Forexample, there are a steepest descent method, a Newton's method, asimplex method, a neural network, and the like.

In particular, when the equation of (Equation 3c) is used for aregression discrimination function 16(E_(r)) and the equation of(Equation 6) are used for a discrimination evaluation function17(E_(d)), a quadratic programming problem solution method generallyused in SVM may be used. Concretely, a case will be considered that E₁is defined as the equation of (Equation 6), E_(r) is defined as theequation of (Equation 3a), and E combined of E_(d) and E_(r) is definedas the equation of (Equation 7a). In this case, E_(d) can be convertedto E′_(d) shown in an equation of (Equation 8a) by being converted to adual representation after converted to a Lagrangian function. Similarly,E_(r) can be converted into E′_(r) shown in an equation of (Equation 8b)by being converted to a dual representation after converted to aLagrangian function′.

[Mathematical  Formula  8a]                                                        (Mathematical  Formula  8a)$\mspace{79mu}{{E_{d}^{\prime}(a)} = {{\sum\limits_{i = 1}^{N_{d}}a_{i}} - {\frac{1}{2}{\sum\limits_{i = 1}^{N_{d}}{\sum\limits_{j = 1}^{N_{d}}{a_{i}a_{j}t_{i}t_{j}{k\left( {x_{i},x_{j}} \right)}}}}}}}$     where $\mspace{79mu}\left\{ {{{\begin{matrix}{a_{i}\text{:}\mspace{14mu}{Lagrange}\mspace{14mu}{multiplier}} \\{{k\left( {x_{i},x_{j}} \right)}\text{:}\mspace{14mu}{kernel}\mspace{14mu}{of}\mspace{14mu} x_{i}\mspace{14mu}{and}\mspace{14mu} x_{j}} \\{t_{i}\text{:}\mspace{14mu}{class}\mspace{14mu}\left( {{1\mspace{14mu}{or}}\mspace{14mu} - 1} \right)} \\{N_{d}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{discrimination}} \\{0 \leq a_{i} \leq C_{d}} \\{{\sum\limits_{i = 1}^{N_{d}}{a_{l}t_{i}}} = 0}\end{matrix}\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 8b} \right\rbrack}\mspace{495mu}\mspace{500mu}\left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 8b} \right){E_{r}^{\prime}\left( {b,\hat{b}} \right)}} = {{{- \frac{1}{2}}{\sum\limits_{i = 1}^{N_{r}}{\sum\limits_{j = 1}^{N_{r}}{\left( {b_{i} - {\hat{b}}_{i}} \right)\left( {b_{j} - {\hat{b}}_{j}} \right){k\left( {x_{i},x_{j}} \right)}}}}} - {ɛ{\sum\limits_{i = 1}^{N_{r}}\left( {b_{i} + {\hat{b}}_{i}} \right)}} + {\sum\limits_{i = 1}^{N_{r}}{\left( {b_{i} - {\hat{b}}_{i}} \right)t_{i}^{\prime}\mspace{79mu}{where}\mspace{79mu}\left\{ \begin{matrix}{b_{i},{{\hat{b}}_{i}:{{Lagrange}\mspace{14mu}{multiplier}}}} \\{{k\left( {x_{i},x_{j}} \right)}:{{kernel}\mspace{14mu}{of}\mspace{14mu} x_{i}\mspace{14mu}{and}\mspace{14mu} x_{j}}} \\{t_{i}^{\prime}\text{:}\mspace{14mu}{evaluation}\mspace{14mu}{value}} \\{N_{r}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{discrimination}} \\{ɛ\text{:}\mspace{14mu}{error}\mspace{14mu}{acceptable}\mspace{14mu}{range}} \\{{0 \leq b_{i} \leq C_{r}},{0 \leq {\hat{b}}_{i} \leq C_{r}}} \\{{\sum\limits_{i = 1}^{N_{c}}\left( {b_{i} - {\hat{b}}_{i}} \right)} = 0}\end{matrix} \right.}}}} \right.$

Based on these conversions, E can be converted to dual representation E′of an equation shown as (Equation 9).

[Mathematical  Formula  9]                                                          (Mathematical  Formula  9)${E^{\prime}\left( {a,b,\hat{b}} \right)} = {{c_{1}\left( {{\sum\limits_{i = 1}^{N_{d}}a_{i}} - {\frac{1}{2}{\sum\limits_{i = 1}^{N_{d}}{\sum\limits_{j = 1}^{N_{d}}{a_{i}a_{j}t_{i}t_{j}{k\left( {x_{i},x_{j}} \right)}}}}}} \right)} + {c_{2}\left( {{{- \frac{1}{2}}{\sum\limits_{i = 1}^{N_{r}}{\sum\limits_{j = 1}^{N_{r}}{\left( {b_{i} - {\hat{b}}_{i}} \right)\left( {b_{j} - {\hat{b}}_{j}} \right){k\left( {x_{i},x_{j}} \right)}}}}} - {ɛ{\sum\limits_{i = 1}^{N_{r}}\left( {b_{i} + {\hat{b}}_{i}} \right)}} + {\sum\limits_{i = 1}^{N_{r}}{\left( {b_{i} - {\hat{b}}_{i}} \right)t_{i}^{\prime}}}} \right)}}$     where $\mspace{79mu}\left\{ \begin{matrix}{a_{i},b_{i},{{\hat{b}}_{i}:{{Lagrange}\mspace{14mu}{multiplier}}}} \\{{k\left( {x_{i},x_{j}} \right)}\text{:}\mspace{14mu}{kernel}\mspace{14mu}{of}\mspace{14mu} x_{i}\mspace{14mu}{and}\mspace{14mu} x_{j}} \\{t_{i}\text{:}\mspace{14mu}{class}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{discrimination}\mspace{14mu}\left( {{1\mspace{14mu}{or}}\mspace{14mu} - 1} \right)} \\{t_{i}^{\prime}\text{:}\mspace{14mu}{evaluation}\mspace{14mu}{value}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{regression}} \\{N_{d}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{discrimination}} \\{N_{r}\text{:}\mspace{14mu}{the}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{samples}\mspace{14mu}{used}\mspace{14mu}{in}\mspace{14mu}{regression}} \\{ɛ\text{:}\mspace{14mu}{error}\mspace{14mu}{acceptable}\mspace{14mu}{range}} \\{c_{1},{c_{2}\text{:}\mspace{14mu}{priority}\mspace{14mu}{constant}}} \\{{0 \leq a_{i^{\prime}} \leq C_{d}},{0 \leq b_{i} \leq C_{R}},{0 \leq {\hat{b}}_{i} \leq C_{R}}} \\{{{\sum\limits_{i = 1}^{N_{d}}{a_{i}t_{i^{\prime}}}} = 0},{{\sum\limits_{i = 1}^{N_{r}}\left( {b_{i} - {\hat{b}}_{i}} \right)} = 0}}\end{matrix} \right.$

After that, an optimistic estimating equation can be obtained bymaximizing E″ with a Sequential Minimal Optimization (SMO). Here, as asubstitute for the Sequential Minimal Optimization, other methods suchas a chunking, a decomposition method, a protected conjugate gradient orthe like can be employed as long as it is a method that can solve thequadratic programming problem. Here, E may be directly optimized byusing a steepest descent method or the like without converting E to dualrepresentation E′.

<<Convergence Test of Optimization of Combination Evaluation Function>>

The convergence determination unit 22 (FIG. 2) will be explained. In thepresent embodiment, optimization of the combination evaluation function20 is executed only once. However, by giving the optimization result tothe evaluation value substitution table creation unit 11 of theevaluation value conversion unit 10 as feedback, optimization may beexecuted again by recalculating the combination evaluation function. Inthis case, a convergence test is executed on the result of there-optimization and, when convergence is not enough, feedback is furthergiven to the evaluation value substitution table creation unit 11. Whenthe convergence is enough, the estimating equation 24 is output.

<<Application of Estimating Equation>>

The estimating equation application unit 25 (FIG. 1, FIG. 2 or FIG. 3)will be explained. As described above, the characteristic amount 301 ofthe new sample 3 is input to the estimating equation 24 which isobtained by optimizing the combination evaluation function 20(E) and theestimated evaluation value 5 is obtained. This estimated evaluationvalue 5 is an output of this method. According to the presentembodiment, a UPDRS ft score can be estimated from finger-tappingmovement data of a new subject even when whether the subject has PD andthe severity of PD are unknown.

<<Selection of Important Characteristic Amount>>

The important characteristic amount selection unit 23 (FIG. 2) will beexplained. In a process to calculate the estimating equation byoptimizing the combination evaluation function, an importantcharacteristic amount 4 can be selected from a plurality ofcharacteristic amounts. The important characteristic amount 4 indicatesa characteristic amount which has a significant impact when anevaluation value is estimated. There may be more than one importantcharacteristic amounts 4. According to the present embodiment, using acorrelation factor between the estimated evaluation value 5 and thecharacteristic amount x_(n) as a standard, the characteristic amountx_(n) which has the highest correlation factor is determined as theimportant characteristic amount 4.

As a standard to select the important characteristic amount 4, otherindexes may be used. For example, it may be considered that a sum ofsquared residuals between the estimated evaluation value 5 obtained fromthe estimating equation 24 which is obtained by optimizing thecombination evaluation function 20 and the actual evaluation value isused as a standard. Further, a determination factor or an F-measure maybe used as a standard.

The important characteristic amount 4 which is selected in this manneris given to the characteristic amount 201 as feedback and thediscrimination/regression process may be re-executed with only theselected important characteristic amount 4. This prevents amulticollinearity caused by too many characteristic amounts and theaccuracy of the estimating equation improves.

<<System Operation Procedure>>

In the present invention for calculating the estimating equation 24,calculation may be executed only once in the beginning or calculationmay be re-executed every time the sample group is increased or changed.In the former case, the estimated evaluation value 5 can be calculatedwhen the system stores only the calculated estimating equation 24. Inthe latter case, the system needs to store sample group 2 for everycalculations.

<<Evaluation of the Present Invention>>

[Evaluation Procedure of the Present Invention]

An evaluation of the present invention employs a LOO (Leave One Out)method. The LOO method is a method for evaluating by dividing “N” numberof pieces of evaluation data into “N−1” pieces of learning data and onepiece of testing data (N=the number of pieces of the unimpairedgroups+the number of pieces of the patient group). In other words, theevaluation is repeated “N” times as changing the combination so that allpieces evaluation data are used as testing data once. Even if a model islearned with “N” pieces of data without using the LOO method and theaccuracy of the model by evaluating the same “N” pieces of data is high,there is a problem that the accuracy may not always high for unknowndata. The LOO method can solve the problem by recognizing one of thepieces of evaluation data as unknown data and evaluate the accuracy ofthe model correctly.

Here, in order to evaluate the accuracy of the estimated evaluationvalue of the testing data, a new index is introduced. This is becausethe present invention aims to realize a discrimination and a regressionat the same time, and it is inappropriate to compare the accuracy withthe conventional method by focusing only one of the discrimination andthe regression. Thus, a later described index is proposed.

The accuracy of the estimated evaluation value of testing data isevaluated based on an error e from the evaluation scale by the followingmethod. When testing data is selected from the patient group, it is setas e=(estimated evaluation value y_(e)−evaluation scale y_(r))². Whentesting data is selected from the unimpaired group, it is set as e=y_(e)² in case of estimated evaluation value y_(e)>0 and it is set as e=0 incase of y_(e)≤0. This is because, regarding an unimpaired person, theaccuracy of the estimated evaluation value is considered to be higherwhen the evaluation scale becomes closer to 0 that the estimatedevaluation value indicates unimpaired in the unimpaired group. Thiserror e is calculated for every piece of the testing data of the LOOmethod and an average value thereof is used as the accuracy of theestimated evaluation value. With this definition, when e becomessmaller, the accuracy of the estimated evaluation value becomes higher.Here, besides the above index, other indexes may be used to evaluate aslong as the index can evaluate a performance of regression anddiscrimination. Further, the performances of the regression anddiscrimination may be evaluated separately.

In this study, in addition to the present invention as a method topropose, a conventional method (using a discrimination analysis and amultiple regression analysis in parallel) is also applied, and theaccuracy of severity quantifications in both methods are compared bysuing the above error e. Here, in addition to the evaluation with theerror e, an evaluation of the discrimination accuracy is also executedwith a sensitivity (a ratio to discriminate patient group and disorder)and a specificity (a ratio to discriminate the unimpaired group asdisorder). Further, in order to observe a data distribution, theevaluation is executed by applying a model leaned from the “N” pieces ofdata to the same “N” pieces of data and plotting the estimatedevaluation value of all pieces of data, without using the LOO method.

[Application Result of the Present Invention]

A result of applying the present invention to finger-tapping movementdata of an unimpaired group and a PD patient group will be described.Then, a result of applying the same data to a conventional method (afterdiscriminating the unimpaired group and PD patient group by adiscrimination analysis, calculating evaluation values for only the PDpatient group by a multiple regression analysis) will be described andcompared with the result of the present invention.

<Result of Applying the Present Invention>

FIG. 15(a) is a result of applying the present invention. The horizontalaxis represents the UPDRS ft score which is an evaluation scale and thevertical axis represents the estimated evaluation value which is outputin the present invention. The sign + represents the unimpaired group andthe sign ∘ represents the PD patient group. Since the UPDRS ft scores ofthe unimpaired group are not evaluated, it is plotted as UPDRS ft=0.This result indicates a result of learning a model of “n” pieces of datawithout using the LOO method and calculating an evaluation value byapplying the same data to the model.

Next, a table illustrated in FIG. 15(b) is a table showing adiscrimination accuracy of the estimated evaluation value by the presentinvention using the LOO method. It is understood that the sensitivity (aratio to discriminate patient group and disorder) is 100.0% and thespecificity (a ratio to discriminate the unimpaired group as disorder)is 81.6%. A table illustrated in FIG. 15(c) is a table showing a resultof evaluating the accuracy of the severity quantification by the presentinvention using the LOO method. It is understood that the unimpairedgroup is 0.371, the PD patient group is 3.290, and the entire result is1.648.

<Result of Application of Conventional Method>

FIGS. 16(a-1) and 16(a-2) are results of applying the conventionalmethod (a method for calculating an evaluation value by applying amultiple regression analysis only for the patient group afterdiscriminating the unimpaired group and the patient group by adiscrimination analysis) to finger-tapping movement data of theunimpaired group and PD patient group. The horizontal axis representsthe UPDRS ft scores as an evaluation scale (an evaluation scale scoredby a doctor) and the vertical axis represents the estimated evaluationvalues output by the multiple regression analysis. The sign + representsthe unimpaired group and the sign ∘ represents the PD patient group.Since the UPDRS ft score is not evaluated in the unimpaired group, it isplotted as UPDRS ft=0. FIG. 16(a-1) is a chart that plots only datawhich is discriminated as a patient group in a discrimination analysis.FIG. 16(a-2) is a chart that plots all pieces of data regardless of thediscrimination result of discrimination analysis. Here, the results ofFIGS. 16(a-1) and 16(a-2) are results of calculation of an evaluationvalue by learning a model based on “N” number of data without using theLOO method and applying the same data to the model.

The chart shown in FIG. 15(b) is a chart that shows the discriminationaccuracy of the conventional method using the LOO method. It isunderstood that the sensitivity is 89.3% and the specificity is 93.4%.The chart of FIG. 16(c) is results of evaluation of the accuracy of theseverity quantification of the present invention using the LOO method.It is understood that the unimpaired group is 6.970, the PD patientgroup is 5.537, and the entire data is 6.027.

<Comparison of Result of the Present Invention and Result ofConventional Method>

When comparing the discrimination result (the chart of FIG. 15(b)) ofthe present invention with the discrimination result (the table of FIG.16(b)) by the discrimination analysis which is a first process of theconventional method, it is understood that the sensitivity is lower by10.7% and the specificity is higher by 7.8% in the discriminationaccuracy. Based on the above, it is understood that the presentinvention is preferable to discriminate the patient group when thediscrimination between the patient group and unimpaired group isambiguity, comparing with the discrimination analysis. In other words,according to the present invention, it is capable of widely detectingsubjects having a possibility of disorder and it may be considered as analgorism preferable to a screening test. Further, comparing the table ofFIG. 15(c) with the table of FIG. 16(c), the present invention has theaccuracy of the estimated evaluation value for the unimpaired groupabout 20 times higher than the conventional method and also has theentire accuracy for the unimpaired group and patient group equal to orgreater than three times higher of the conventional method.

Next, based on the data distributions (FIG. 15(a) and FIG. 16(a-1)),validity of the estimated evaluation values will be examined. Based onFIG. 16(a-1), it is considered that the estimated evaluation values areinvalid since the estimated evaluation value of data of an unimpairedperson who is discriminated as the patient group is greater than zerowhich indicates to be unimpaired and is a value equivalent to theestimated evaluation values of the PD patient group. On the other hand,based on FIG. 15(a), according to the present invention, the estimatedevaluation values of the unimpaired group are close to zero and found inthe same level with the data of those in the PD patient group with mildsymptom. Based on the above, it can be considered that the estimatedevaluation value of the present invention are valid.

Further, FIG. 16(a-2) shows a case of calculating an estimatedevaluation value for data discriminated as an unimpaired group, based onthe result of the multiple regression analysis. In this case, it isunderstood that there are samples whose estimated evaluation values arecalculated greater than zero and equal to or higher than the level ofthe PD patient group, even it belongs to the unimpaired group. It can beconsidered that it is invalid that the estimated evaluation value isabnormally high even though it is discriminated as the unimpaired group.The reason is considered that the generalizability is lowered becauseonly the data of a patient group in the multiple regression analysiswhich is a second process of the conventional method. Concretely,according to the present invention, it is considered that thegeneralizability can be increased since regression is executedconsidering the discrimination of the easily-taken unimpaired group andpatient group, not only the patient group whose data amount isinsufficient.

Summarizing the above, the present invention has a higher accuracy ofthe severity quantification compared to the conventional method.Further, the conventional method calculates an estimated evaluationvalue for data discriminated to be a patient group; however, the presentinvention can calculate an evaluation value regardless of the severityof symptom. With these points, it can be said the present invention issuperior to the conventional method.

<Selection Result of Important Characteristic Amount>

In the used 21 characteristic amounts, the characteristic amounts havingthe highest correlation factor with the estimated evaluation value wasthe standard deviation (21) of tapping intervals and the correlationfactor was 0.4595. It is thus understood that the standard deviation(21) of tapping intervals is the most important characteristic amountwhen an evaluation value is estimated.

Second Embodiment

A second embodiment will be explained in detail with reference todrawings according to need. In the present embodiment, adiscrimination/regression process for a plurality of evaluation valueswill be described based on the discrimination/regression processexplained in the first embodiment. Hereinafter, particularly, a case forestimating severities of two types of disorders will be explained.

FIG. 17 illustrates a flow of a case for quantifies severities of twotype of disorders (patient group 1 and patient group 2). The explanationwill be made from the top of the diagram. Firstly, with a methoddescribed in the first embodiment, for the patient group 1, a regressionevaluation function 16 (E_(r)?), a discrimination evaluation function 17(E_(d1)), and a combination evaluation function 20 (E₁) are calculated.Similarly, also for the patient group 2, a regression evaluationfunction 16 (E_(r2)), a discrimination evaluation function 17 (E_(d2)),and a combination evaluation function 20 (E₂) are calculated. Next,using E₁ and E₂, a combination evaluation function 2006 (E′) with anindependent condition is calculated. The part for calculating E′ is adifferent point of the present embodiment from the first embodiment. Amethod for calculating E′ will be described.

The combination evaluation function 2006 (E′) with an independentcondition is defined by (Equation 10).[Mathematical Formula 10]E′=(1−c _(s))E ₁ +c _(s) E ₂ +c _(o) |T _(o)|  (Mathematical Formula 10)

Here, E₁ is a combination evaluation function for a discriminationregression of the unimpaired group and patient group 1, and E₂ is acombination evaluation function for a discrimination regression for theunimpaired group and the patient group 2. c_(s) is a constant (adisorder priority constant 2014) for adjusting the priority of aseverity quantification of the disorder 1 and a severity quantificationof the disorder 2. c_(s) is equal to or greater than zero and equal toor less than one. c_(s) is set as a small value when focusing theaccuracy of the severity quantification of the disorder 1 and set as alarge value when focusing the accuracy f the severity quantification ofthe disorder 2. T₀ is a variable that expresses a condition that theseverity of the patient group 1 and the severity of the patient group 2become independent (hereinafter, referred to as an independentcondition) and will be described in detail. c₀ is a constant thatdefines strength of the independent condition (independent conditionconstant 2012). c₀ is set as a numerical value equal to or greater thanzero. The greater value is set as c₀, it comes closer to a conditionthat y_(e1) and y_(e2) are exactly orthogonal.

The above T₀ will be explained. T₀ is defined by an independentcondition defining unit 2011. T₀ is a variable that expresses that anestimated severity f the patient group 1 and an estimated severity ofthe patient group 2 become independent. Here, the condition in which theseverity of each patient are independent means that the disorder 1 anddisorder 2 are not related with each other and they will never bedeveloped at the same time. In other words, when the severity y_(e1) ofthe patient is high, the severity y_(e2) of the disorder 2 is low andwhen the severity y_(e2) of the disorder 2 is high, the severity y_(e1)of the disorder 1 is low.

The independent condition is a case that an axis of the severity of thepatient group 1 and an axis of the severity of the patient group 2 areorthogonal. A pattern diagram of this case is illustrated in FIG. 18. InFIG. 18, a case that the number of characteristic amounts is three isillustrated for the sake of convenience. Thus, the independent conditioncan be expressed as an inner product of the axis y_(e1) (2201) of theestimated severity of the disorder 1 and the axis y_(e2) (2202) of theestimated severity and the disorder 2, as expressed in (Equation 11).

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 11} \right\rbrack\mspace{185mu}} & \; \\{\mspace{79mu}{T_{o} = \frac{y_{e\; 1} \cdot y_{e\; 2}}{{y_{e\; 1}}{y_{e\; 2}}}}} & \left( {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 11} \right)\end{matrix}$

Here, for example, T₀ is defined as a cosine of y_(e1) and y_(e2) asfollows. T₀ is in a range of −1<T₀<1 according to the angle betweeny_(e1) and y_(e2). When y_(e1) and y_(e2) face in the same direction, itbecomes T₀=1, and when y_(e1) and y_(e2) face in different directions,it becomes T₀=−1. When y_(e1) and y_(e2) are orthogonal, it becomesT₀=0. In other words, only when y_(e1) and y_(e2) are orthogonal, itbecomes |T₀|=0, and |T₀| becomes larger as the condition becomes furtherfrom the orthogonal condition. Thus, it is understood that |T₀| isminimized to make the condition closer to the condition that y_(e1) andy_(e2) are orthogonal. In (Equation 11), in a case that a large value isset as c₀, |T₀| becomes a small value when E′ is minimized and y_(e1)and y_(e2) come close to a condition of being orthogonal. In contrast,in a case that a small value is set as c₀, |T₀| becomes a large valuewhen E′ is minimized and y_(e1) and y_(e2) come further from a conditionof being orthogonal. Here, as the definition of T₀, the above method isnot needed to be used, and any method that can express independence ofthe axes of the a plurality of severity can be used.

Similarly to the first embodiment, E′ defined as the above is optimizedby the evaluation function optimization unit 21. As a result, anestimating equation 1 (2007) of the severity of the disorder 1 and anestimating equation 2 (2008) of the severity of the disorder 2 can beobtained. With this, the severity (estimated evaluation value 2009) ofthe disorder 1 and the severity (estimated evaluation value 2010) of thedisorder 2 can be obtained. Here, regarding above c_(s), a numericalvalue can be set in advance or a numerical value having a high estimatedaccuracy of the severity may be searched (disorder priority constantsearch unit 2015). Similarly, regarding above c₀, a numerical value maybe set in advance or a numerical value having a high estimated accuracyof the severity may be searched (independent condition constant searchunit 2013). Further, the disorders described in the present embodimentare two types; however, the idea of the present study may be expandedfor three or more types. For example, there may be a method thatevaluates independences by combining a pair of axes and obtains T₀ byadding the independences.

REFERENCE SIGNS LIST

-   2 sample group-   3 new sample-   4 important characteristic amount-   5 estimated evaluation value-   11 evaluation value substitution table creation unit-   12 evaluation value substitution unit-   13 sample assignment unit-   14 regression evaluation function calculation unit-   15 discrimination evaluation function calculation unit-   16 regression evaluation function-   17 discrimination evaluation function-   18 combination evaluation function calculation unit-   19 discrimination/regression priority adjusting unit-   20 combination evaluation function-   21 combination evaluation function optimization unit-   22 convergence determination unit-   23 important characteristic amount selection unit-   24 estimating equation-   25 estimating equation application unit-   41 state that two fingers are closed-   42 state that two fingers are opened-   43 magnetic sensor-   44 distance between two fingers-   51 distance waveform-   52 velocity waveform-   53 acceleration waveform-   101 discrimination process-   102 regression process-   103 sample group (1)-   104 new sample-   105 sample group (2)-   106 estimated evaluation value-   201 characteristic amount of sample group-   202 evaluation value of sample group-   301 characteristic amount of new sample-   1011 discrimination evaluation function calculation unit-   1012 discrimination evaluation function-   1013 discrimination evaluation function optimization unit-   1014 discrimination equation-   1015 discrimination equation application unit-   1021 regression evaluation function calculation unit-   1022 regression evaluation function-   1023 regression evaluation function optimization unit-   1024 estimating equation-   1025 estimating equation application unit-   1901 priority constant-   1902 priority constant search unit-   2001 unimpaired group-   2002 patient group 1-   2003 unimpaired group-   2004 patient group 2-   2005 independent-condition combination evaluation function    calculation unit-   2006 independent-condition combination evaluation function-   2007 estimating equation 1-   2008 estimating equation 2-   2009 estimated evaluation value 1-   2010 estimated evaluation value 2-   2011 independent condition defining unit-   2012 independent condition constant-   2013 independent condition constant search unit-   2014 disorder priority constant-   2015 disorder priority constant search unit-   2201 axis of estimated severity of disorder 1-   2202 axis of estimated severity of disorder 2-   10301 characteristic amount of sample group (1)-   10401 characteristic amount of new sample-   10501 characteristic amount of sample group (2)-   10502 evaluation value of sample group (2)

The invention claimed is:
 1. An estimated evaluation value calculationsystem for calculating an estimated evaluation value from a new subjectcomprising: a measurement device configured to obtain a measurementoriginal data from the new subject; a memory configured to store aplurality of pairs of a characteristic amount that is calculatednumerically based on the measurement original data each of which waspreviously measured from a plurality of subjects and an evaluation valuethat is corresponding to the characteristic amount; and a processingdevice; wherein said processing device includes: a regression evaluationfunction calculation unit configured to calculate a regressionevaluation function that represents a power of errors between theevaluation value and the estimated evaluation value, based on theplurality of pairs of the characteristic amount and the correspondingthe evaluation value stored in said memory a discrimination evaluationfunction calculation unit configured to calculate a discriminationevaluation function that evaluates accuracy of dividing the plurality ofpairs of the characteristic amount and the corresponding the evaluationvalue stored in said memory into a class of an unimpaired group and aclass of a patient group depending on each of the characteristicamounts; a combination evaluation function calculation unit configuredto calculate a combination evaluation function by combining theregression evaluation function and the discrimination evaluationfunction; a combination evaluation function optimization unit configuredto calculate a factor of an estimating equation for calculating anestimated evaluation value by optimizing the combination evaluationfunction; a characteristic amount extraction device configured tocalculate numerically the measurement original data from saidmeasurement device to obtain a characteristic amount of the new subject;an estimating equation application unit configured to calculate theestimated evaluation value by applying a characteristic amount of thenew subject to the estimating equation; and an output unit configured tooutput said estimated evaluation value calculated by said estimatingequation application unit.
 2. The estimated evaluation value calculationsystem according to claim 1, wherein the combination evaluation functioncalculation unit comprises a discrimination/regression priorityadjusting unit configured to calculate the combination evaluationfunction by using a predetermined constant that adjusts the priority ofthe regression evaluation function and the discrimination evaluationfunction.
 3. The estimated evaluation value calculation system accordingto claim 2, wherein the discrimination/regression priority adjustingunit comprises a priority constant search unit configured to determinethe priority constant that optimizes the combination evaluationfunction.
 4. The estimated evaluation value calculation system accordingto claim 1 comprising: an evaluation value substitution table creationunit configured to create a table that associates a numerical valuedistribution with the evaluation value or associates a numerical valuedistribution when an evaluation value is absent; an evaluation valuesubstitution unit configured to substitute evaluation values of a partof or all of the samples with the numerical value distributionassociated by the evaluation value substitution table creation unit, orgive evaluation values of a part or all of samples lacking an evaluationvalue as the numerical value distribution associated by the evaluationvalue substitution table creation unit; and a sample assignment unitconfigured to assign the sample group, in which the evaluation value issubstituted, to the regression evaluation function calculation unit andthe discrimination evaluation function calculation unit.
 5. Theestimated evaluation value calculation system according to claim 4,wherein the numerical value distribution of the evaluation valuesubstitution table creation unit is expressed by a numerical value rangewhich is limited by one or more inequality expression.
 6. The estimatedevaluation value calculation system according to claim 4, wherein thenumerical value distribution of the evaluation value substitution tablecreation unit is expressed by a numerical value function using theevaluation value as an input.
 7. The estimated evaluation valuecalculation system according to claim 4, wherein the numerical valuedistribution of the evaluation value substitution table creation unit isa numerical value different from the evaluation value.
 8. The estimatedevaluation value calculation system according to claim 4, furthercomprising a convergence determination unit configured to execute aconvergence test after the combination evaluation function optimizationunit and, when it is not converged, gives feedback to the evaluationvalue substitution table creation unit to correct the table.
 9. Theestimated evaluation value calculation system according to claim 1,wherein the regression evaluation function calculated in the regressionevaluation function calculation unit is a summation of the power oferrors between the evaluation value and estimated evaluation value ofthe sample group.
 10. The estimated evaluation value calculation systemaccording to claim 1, further comprising an important characteristicamount selection unit configured to select a characteristic amount whichis closely related to the estimated evaluation value from thecharacteristic amount.
 11. The estimated evaluation value calculationsystem according to claim 1, further comprising: an independentcondition defining unit configured to define a condition that aplurality of types of evaluation values are independent from each other;and an independent-condition combination evaluation function calculationunit configured to calculate a combination evaluation function with anindependent condition by using the independent condition and thecombination evaluation function corresponding to a plurality of types ofevaluation values.
 12. The estimated evaluation value calculation systemaccording to claim 11, wherein the independent condition defining unitconfigured to define the condition that the two types of evaluationvalues become independent from each other with a cosine which isobtained by dividing an inner product of axis vectors of the two typesof evaluation values by absolute values of the respective axis vectors.13. The estimated evaluation value calculation system according to claim11, wherein the independent-condition combination evaluation functioncalculation unit calculates the combination evaluation function with anindependent condition by using an independent condition constant whichindicates strength of the independent condition.
 14. The estimatedevaluation value calculation system according to claim 13, furthercomprising an independent condition constant search unit configured tosearch the independent condition constant used in theindependent-condition combination evaluation function calculation unit.15. An estimated evaluation value calculation method for calculating anestimated evaluation value from a new subject comprising: measuring, bya measurement device, to obtain a measurement original data from the newsubject; storing, by a memory, a plurality of pairs of a characteristicamount that is calculated numerically based on the measurement originaldata each of which was previously measured from a plurality of subjectsand an evaluation value that is corresponding to the characteristicamount and a processing device; wherein said processing device isconfigured to execute the following steps: calculate, by a regressionevaluation function calculation unit, a regression evaluation functionthat represents a power of errors between the evaluation value and theestimated evaluation value, based on the plurality of pairs of thecharacteristic amount and the corresponding the evaluation value storedin said memory; calculate, by a discrimination evaluation functioncalculation unit, a discrimination evaluation function that evaluatesaccuracy of dividing the plurality of pairs of the characteristic amountand the corresponding the evaluation value stored in said memory into aclass of an unimpaired group and a class of a patient group depending oneach of the characteristic amounts; calculate, by a combinationevaluation function calculation unit, a combination evaluation functionby combining the regression evaluation function and the discriminationevaluation function; calculate, by a combination evaluation functionoptimization unit, a factor of an estimating equation for calculating anestimated evaluation value by optimizing the combination evaluationfunction; calculate, by a characteristic amount extraction device,numerically the measurement original data from said measurement deviceto obtain a characteristic amount of the new subject; calculate, by anestimating equation application unit, the estimated evaluation value byapplying a characteristic amount of the new subject to the estimatingequation; and calculate, by an output unit, said estimated evaluationvalue calculated by said estimating equation application unit.